Blockchain and Computational Intelligence Inspired Incentive-Compatible Demand Response in Internet of Electric Vehicles

摘要

By leveraging the charging and discharging capabilities of Internet of electric vehicles (IoEV), demand response (DR) can be implemented in smart cities to enable intelligent energy scheduling and trading. However, IoEV-based DR confronts many challenges, such as a lack of incentive mechanism, privacy leakage, and security threats. This motivates us to develop a distributed, privacy-preserved, and incentive-compatible DR mechanism for IoEV. Specifically, we propose a consortium blockchain-enabled secure energy trading framework for electric vehicles (EVs) with moderate cost. To incentivize more EVs to participate in DR, a contract theory-based incentive mechanism is proposed, in which various contract items are tailored for the unique characteristics of EV types. The contract optimization problem falls into the category of difference of convex programing, and is solved by using the iterative convex-concave procedure algorithm. Furthermore, we consider the scenario where the statistical knowledge of the EV type is unknown. In such a case, we demonstrate how to derive the probability distribution of the EV type by exploring computational intelligence-based state of charge estimation techniques, e.g., Gaussian process regression. Finally, the security and efficiency performance of the proposed scheme is analyzed and validated.